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68 lines
2.7 KiB
68 lines
2.7 KiB
5 months ago
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import os
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from tqdm import tqdm
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import torch
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from torch.utils.data import DataLoader
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from logger import Logger, Visualizer
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import numpy as np
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import imageio
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from sync_batchnorm import DataParallelWithCallback
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def reconstruction(config, generator, kp_detector, checkpoint, log_dir, dataset):
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png_dir = os.path.join(log_dir, 'reconstruction/png')
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log_dir = os.path.join(log_dir, 'reconstruction')
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if checkpoint is not None:
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Logger.load_cpk(checkpoint, generator=generator, kp_detector=kp_detector)
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else:
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raise AttributeError("Checkpoint should be specified for mode='reconstruction'.")
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dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
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if not os.path.exists(log_dir):
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os.makedirs(log_dir)
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if not os.path.exists(png_dir):
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os.makedirs(png_dir)
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loss_list = []
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if torch.cuda.is_available():
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generator = DataParallelWithCallback(generator)
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kp_detector = DataParallelWithCallback(kp_detector)
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generator.eval()
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kp_detector.eval()
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for it, x in tqdm(enumerate(dataloader)):
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if config['reconstruction_params']['num_videos'] is not None:
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if it > config['reconstruction_params']['num_videos']:
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break
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with torch.no_grad():
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predictions = []
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visualizations = []
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if torch.cuda.is_available():
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x['video'] = x['video'].cuda()
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kp_source = kp_detector(x['video'][:, :, 0])
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for frame_idx in range(x['video'].shape[2]):
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source = x['video'][:, :, 0]
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driving = x['video'][:, :, frame_idx]
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kp_driving = kp_detector(driving)
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out = generator(source, kp_source=kp_source, kp_driving=kp_driving)
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out['kp_source'] = kp_source
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out['kp_driving'] = kp_driving
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del out['sparse_deformed']
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predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
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visualization = Visualizer(**config['visualizer_params']).visualize(source=source,
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driving=driving, out=out)
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visualizations.append(visualization)
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loss_list.append(torch.abs(out['prediction'] - driving).mean().cpu().numpy())
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predictions = np.concatenate(predictions, axis=1)
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imageio.imsave(os.path.join(png_dir, x['name'][0] + '.png'), (255 * predictions).astype(np.uint8))
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image_name = x['name'][0] + config['reconstruction_params']['format']
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imageio.mimsave(os.path.join(log_dir, image_name), visualizations)
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print("Reconstruction loss: %s" % np.mean(loss_list))
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